Efficient Adaptive Data Acquisition via Pretrained Belief Representations

arXiv:2606. 25197v1 Announce Type: cross Abstract: Learning effective policies for adaptive data acquisition remains challenging: posterior-based methods rely on surrogate models and posterior approximations that can be misspecified or biased, while direct policy-learning methods map from historical observations and fail to exploit available model representations, making learning harder.